Program 2CApplications Closed

AI Testing

The next frontier of QA is testing AI-powered systems, and testing with AI tools.

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Students learning AI Testing at Testify Academy

Practical learning. Expert feedback. Career ready outcomes.

Next cohortMon, 19 October 2026
Duration12 weeks
FormatHybrid / Remote
Tuition₦119,000
Program overview

What this program prepares you to do

Prepare to be the tester every AI-driven product team is scrambling to hire.

AI Testing covers both halves of the new job: testing products that contain machine learning, and using AI tooling to accelerate your own testing without quietly faking coverage.

Testing a system whose output changes between identical runs breaks the pass or fail habit. You will build evaluation sets, probe for hallucination and bias, and learn to report confidence rather than certainty.

The curriculum is refreshed continuously and includes Cycluno case studies from AI features already running in production.

Who this is for

  • You already test software and want the AI specialisation early
  • Your product team is shipping AI features you are expected to sign off
  • You want AI assistance in your workflow used responsibly

Outcomes

  • Test machine learning and large language model features methodically
  • Build evaluation sets and scoring rubrics for non deterministic output
  • Probe systematically for hallucination, bias and unsafe behaviour
  • Generate and review test assets with AI tooling, safely
  • Advise a product team on AI release risk in writing
Syllabus

Learn by building, module by module

01
Module 1

How AI systems behave, and why testing changes

The mental model. Models, prompts, context, retrieval and where each one fails.

  • •Machine learning and language model basics for testers
  • •Non determinism, temperature and variance
  • •Retrieval, grounding and context failure
  • •New risk classes AI introduces to a product

Project: A risk map for an AI feature, with the failure modes named.

02
Module 2

Evaluation and test design for AI features

Replacing exact expected results with graded evaluation.

  • •Building golden datasets and evaluation sets
  • •Rubric design and scoring consistency
  • •Regression testing across model or prompt changes
  • •Automated evaluation harnesses

Project: A working evaluation set with rubric and baseline scores.

03
Module 3

Hallucination, bias and safety probing

Adversarial work. Deliberately trying to make the system misbehave, then documenting it.

  • •Hallucination detection and grounding checks
  • •Bias and fairness probing across user groups
  • •Prompt injection and misuse scenarios
  • •Documenting findings responsibly

Project: An adversarial test report with reproducible cases.

04
Module 4

Testing with AI tooling

Using AI to move faster while staying accountable for coverage.

  • •Prompt based test case and data generation
  • •AI assisted exploratory testing and review
  • •Verifying AI generated assets before you trust them
  • •Data and confidentiality boundaries

Project: A generated and human verified test suite with a coverage audit.

05
Module 5

Release decisions for AI products

Signing off something that cannot be proved correct.

  • •Acceptance thresholds and confidence reporting
  • •Monitoring, guardrails and production evaluation
  • •Incident response for model behaviour
  • •Advising stakeholders on residual risk

Project: A written release recommendation for an AI feature, defended live.

06
Final module

Project week and live defence

You stop consuming the curriculum and produce evidence of it. You take an AI powered feature and produce the full evaluation and risk picture.

  • •Scoping the project brief with your mentor
  • •Working the project through a full cycle, with checkpoints
  • •Preparing the written artefacts a reviewer will read
  • •Presenting and defending your decisions live

Project: A defended AI testing project, certificate and report card.

How admission works

From application to certification

  1. 01

    Confirm this is the program you want to take.

  2. 02

    Make payment: one time in full, or split into two installments.

  3. 03

    We send you a mail confirmation with your admission letter.

  4. 04

    Sign the letter and send it back to us.

  5. 05

    You get added to the cohort rooms.

  6. 06

    Participate in the onboarding session.

  7. 07

    Learn flexibly: self study during the week, submit your assignment by 12pm (WAT) every Friday, and attend the weekly live review class with your mentor.

  8. 08

    Continue until module / project week, group or personal. You work actively on the project and defend it live before invited professional leaders.

  9. 09

    At the end of the program, you receive your certificate and report card.

Option A, Pay in full

₦119,000

One single payment covering the whole program. Confirms your seat immediately and is the best value option.

Option B, Two installments

2 × ₦70,000

First installment is due before onboarding and secures your seat. The second installment is due before the mid-program module / project week; access to the next module pauses until it is settled.

Next cohort timeline

Mon, 28 September 2026

Application deadline

Apply and complete payment (in full or first installment) to hold your seat for this cohort.

Tue, 29 September 2026

Admission letter

Within 24 hours of payment you receive a payment confirmation mail with your admission letter attached.

Thu, 1 October 2026

Signed letter due

Sign the admission letter and send it back to us within 48 hours of receiving it.

Fri, 2 October 2026

Cohort rooms access

You are added to your cohort rooms (WhatsApp and class room) and get your named mentor's details.

Fri, 16 October 2026

Live onboarding session

We walk through the weekly rhythm, the Friday 12pm (WAT) assignment deadline, and your project week.

Mon, 19 October 2026

Cohort start date

Module 1 opens. Self study during the week, assignment by Friday 12pm (WAT), live review class with your mentor.

Fri, 23 October 2026

First assignment due

Your first weekly assignment is due at 12pm (WAT) on the Friday of week one.

Completion requirements

  • If you don't complete a module / class with evidence of a defended project or completed assignments averaging a 75% pass score across all module lessons, you won't be granted access to proceed to the next module / class.
  • No completion automatically means no certification.
FAQ

Questions about this program